Aristolochic acid (AA) is a component of traditional Chinese herbs and commonly used in the farm poultry industry in China for anti-infection, anti-viral and anti-bacterial treatment. However, long-term and over-exposure of these drugs has been proven to be associated with serious hepatotoxicity, but the mechanism of AA-I-induced hepatotoxicity remains unknown. Therefore, in this study, a subchronic toxicity test was conducted to evaluate the mechanism of AA-I-induced hepatotoxicity in Tianfu broilers. Subchronic exposure to high doses of AA-I in broilers can cause serious hepatotoxicity by breaking the redox balance to form oxidative stress, along with promoting oxidative-stress-mediated apoptosis and mitochondrial damage. In conclusion, AA-I has been found to damage broilers’ livers in high doses. This study provides suggestions for the clinical application of traditional Chinese medicine containing AA-I in the poultry industry.Aristolochic acid (AA) is a component of traditional Chinese herbs and commonly used for farm animals in China. Over-exposure of AA has been proven to be associated with hepatotoxicity; however, the mechanism of action of AA-I-induced hepatotoxicity remains unknown. In the current study, a subchronic toxicity test was conducted to evaluate the mechanism of AA-induced hepatotoxicity in Tianfu broilers. According to the results, AA-I-induced hepatotoxicity in Tianfu broilers was evidenced by the elevation of liver weight, levels of serum glutamic oxalacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT). Furthermore, hepatocyte swelling, vesicular degeneration and steatosis were observed. Additionally, AA-I elevated the production of reactive oxygen species (ROS) and induced oxidative stress, which further led to excessive apoptosis, characterized by mitochondrial depolarization, upregulation of Bax, and down-regulation of Bcl-2 expression. In conclusion, the mechanism of AA-I-induced hepatotoxicity was associated with oxidative-stress-mediated apoptosis and mitochondrial damage. Aristolochic acids (AAs) are chemical components of various natural herbs, especially in Aristolochiaceae . AA exerThe use of botanical drugs containing AA has been prohibited in many countries due to its toxic effects . HoweverExogenous chemicals can be metabolized to active metabolites by hepatic enzymes in the liver, which may further induce free radicals and lead to peroxidation. Hepatocyte apoptosis and necrosis can be triggered by the aforementioned active metabolites, leading to hepatic injury and hepatic carcinogenesis . MoreoveThe LD50 of AA-I to induce subchronic toxicity in Tianfu broilers has been determined in a previous study . The aimAristolochic acid A  was purchased from Chengdu Rifens Tech-nology Co., Ltd., Sichuan, China. TRIzol reagent , total ROS assay kit , MMP detection JC-1 kit , eBioscienceTM Annexin V-FITC Apoptosis Kit , and reverse transcription reagents  were purchased from the relevant companies. The chicken-specific ELISA assay kits for glutamic oxalacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), total antioxidant capacity (T-AOC), malondialdehyde (MDA), superoxidase dismutase (SOD) and glutathione (GSH) were purchased from Nanjing Jiancheng Bioengineering Institute .The study has been approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University (permission number DYY-2018203007) and was conducted at the poultry farm of Sichuan Agricultural University. Animals and procedures were conducted in accordance with the regulations of the national standard Laboratory Animal Requirements of Environment and Housing Facilities (GB 14925–2001).Forty, 1-day-old, male Tianfu broilers with body weight of 38.83 ± 3.45 g from the Poultry Breeding Farm of Sichuan Agricultural University  were used in this study. All subjects were captive in a climate-controlled facility with air-ventilator and the relative humidity was kept at 50%. The photoperiod was maintained at 24 h for the first 14 days, followed by 20 h for the rest of the study. The temperature was set at 34 °C initially, followed by decremented 2 °C per week until the temperature reached 26 °C, which was maintained throughout the study. Food and water were free to access throughout the study. The daily diet formulation was determined according to the National Research Council requirements for chickens  22]..22].Three different doses  of LD50, with these amounts determined in a previous study , were usForty, 1-day-old, male broilers were randomly divided into four groups. Subjects in the control group (CG) received normal saline. AA-I at 1/100, 1/50, and 1/10 of LD50 was administered to subjects in the low-dose group (LAG), middle-dose group (MAG), and high-dose group (HAG), respectively. Drugs were administered intraperitoneally in all groups. Subjects were acclimated in the facility for 24 h (D 0), followed by the administration of experimental agents once daily (q 24 h) for 28 days (D 1 to D 28).All subjects were sacrificed, and samples were collected on D 29. The liver was weighed and the ratio of liver weight to body weight (g/kg) (relative liver index) was calculated.w/v) buffered paraformaldehyde for histology. The remaining liver samples were flushed by 0.9% normal saline for biochemical analysis. The harvested hepatic cells were used for flow cytometry analysis. The method of harvesting hepatic cells has been described elsewhere [Liver samples were fixed in 4% , JC-1, and 5 μL of Annexin V-FITC and 5 μL of PI, respectively. All hepatocytes were incubated in a dark environment at 37 °C for 15 min. A cytometry flowmeter  was used to detect reactive oxygen species , mitochondrial membrane potentials (MMPs) and the percentage of apoptosis. The MMP result was described as a mitochondrial depolarization ratio. All aforementioned data were translated and recorded by Kaluza 2.1 Software, Beckman Coulter, CA, USA.The observation criteria of ultrastructure were published elsewhere . Liver sSamples were rinsed by pre-frozen Diethypyrocarbonate (DEPC) water and then frozen in liquid nitrogen at −80 °C. Approximately 60 mg of frozen sample was ground thoroughly in a precooling tissue homogenizer. Total RNA was extracted by a commercial kit . The RNA concentration was detected by a nucleic acid protein analyzer which had the D260/D280 reverse transcription eligible range at 1.8–2.0. The cDNA was stored at −80 °C.target gene − Ct housekeeping gene. Primers used in the present study , and the 2−ΔΔCt method was applied for quantifying gene expressions, where ΔΔCt is the detected ΔCt- reference ΔCt and ΔCt is the Ct nt study  were desp < 0.05.All parameters were presented as mean ± standard error (mean ± SE) in this study. Duncan’s test was used to determine the differences among treatment groups. Data were analyzed by one-way analysis of variance (ANOVA) with SPSS 19.0 . Significance was determined when p < 0.05) after intraperitoneal administration of AA-I for 28 days. The liver index results are shown in The macroscopic characteristics of liver samples are shown in p < 0.05), and nearly doubled levels were observed in MAG and HAG (p < 0.05). For serum GPT, both MAG and HAG showed significant elevations of serum GPT in comparison with that of CG (p < 0.05).The serum transaminase levels are shown in The results of the histopathological changes of hepatocytes are shown in p < 0.05). Both GSH and T-AOC in the HAG showed significant decreases when compared to the CG (p < 0.05), whereas compared with the CG, the results revealed that AA-I-induced MDA and ROS accumulation in the liver is due to the significant elevation of MDA and ROS levels in the HAG (p < 0.05). All results are shown in SOD levels decreased significantly in all treatment groups in comparison with the CG (p < 0.05).Furthermore, the relative mRNA levels of antioxidant stress genes were evaluated in the present study. According to the results of QRT-PCR , comparep < 0.05). Compared with the CG, the percentage of hepatocytes apoptosis was significantly elevated in all AA-I treatment groups (p < 0.05).The results of mitochondrial damage and hepatocytes apoptosis are shown in The ultrastructure of hepatocytes is presented in p < 0.05). Additionally, the mRNA expression of Bax increased significantly in all AA-I treatments relative to the control (p < 0.05).The relative mRNA levels of apoptotic genes involved in the mitochondrial apoptotic pathway were also measured in the present study. As shown in Aristolochic acids (AAs) are chemical components which are abundant in traditional Chinese medicines, which have been used commonly as an alternative to antibiotics in the poultry and livestock industries due to the variety of therapeutic effects . HoweverThe liver plays an important role of various biological activities, including bile acid secretion, immunization, detoxification and metabolism . It has Although the hepatotoxicity of AA-I has been widely presented in several works, the comprehension of its underlying mechanism remains unknown. It has been indicated that oxidative stress plays an essential role in AA-I-induced liver damage ,38. OxidOxidative stress is a crucial factor for the induction of apoptosis and excessive ROS promotes cell degradation and death . RomanovTo conclude (as summarized in


Several nanovesicles were prepared and optimized, using four-factor Box–Behnken, in an experimental design. The optimized phytosomes showed vesicle size and zeta potential values of 116.9 nm and 31.5 mV, respectively. The IC50 values revealed that MCF-7 cells were significantly more sensitive to the optimized QRT formula than the plain formula and raw QRT. Cell cycle analysis revealed that optimized QRT formula treatment resulted in significant cell cycle arrest at the S phase. The results also indicated that treatment with QRT formula significantly increased caspase-9, Bax, Bcl-2, and p53 mRNA expression, compared with the plain formula and QRT. In terms of the inflammatory markers, the QRT formula significantly reduced the activity of TNF-α and NF-κB, in comparison with the plain formula and QRT only. Overall, the findings from the study proved that a QRT formulation could be a promising therapeutic approach for the treatment of breast cancer.Breast cancer is a dangerous type of cancer in women. Quercetin (QRT), a naturally occurring flavonoid, has wide biological effects including antioxidant, anticarcinogenic, anti-inflammatory, antiallergic, and antiviral activities. The anticancer activity is considered the most valuable effect of QRT against several types of cancer, including prostate, liver, lung, colon, and breast cancer. Scorpion venom peptides (SV) has been found to induce apoptosis and aggravate cancer cells, making it a promising anticancer agent. QRT, SV, and Phospholipon Phytosomal delivery systems result mainly from the interaction of naturally occurring phospholipids and natural active ingredients . PhytosoIn 2008, the World Health Organization announced that cancer resulted in 7.6 million deaths, exceeding deaths caused by heart disease . TherefoOur hypothesis is based on the combination of QRT and SV producing synergistic anticancer activity. We attempted to load these natural anticancer drugs into a drug delivery system; phytosomes were chosen as a perfect drug delivery system for naturally occurring drugs, such as QRT and SV. The antiproliferative activity of the developed QRT and SV phytosomal were examined through cytotoxicity tests, such as cell cycle analysis, apoptosis tests, caspase-9, and Bax and BCL-2 levels to evaluate and elucidate the superior activity of the systems when compared with free QRT or SV. The antiproliferative and anti-apoptotic activities were evaluated with the use of human breast cancer MCF-7 cell line. A four-factor Box–Behnken experimental design was used to prepare and optimize the different formulations of phytosomal systems, and the optimized formulations were chosen to evaluate the combination of QRT and SV into the phytosomal systems.® 90H  was obtained as a gift sample from Lipoid GmbH . The human tumor cell line MCF-7 used in this study was obtained from the VACSERA  cell culture unit that was originally acquired from ATCC .QRT, SV were obtained from Sigma-Aldrich Inc. , Phospholipon1) and SV amount , in addition to two process variables, namely, process temperature  and reflux time . The upper and lower coded and actual levels of each variable are compiled in p < 0.05. Perturbation plots and two-dimensional contour plots were produced to demonstrate the effect of the investigated variables and explore the interaction between them.The response surface, specifically the four-factor Box–Behnken experimental design was implemented for optimizing the proposed QRT-PHM-SV formulation. The studied independent variables included two formulation factors, namely PL amount  in the specified molar ratios were dissolved in dichloromethane (20 mL). The solution was refluxed at the temperature and time specified according to the experimental design, and then evaporated to obtain a concentrate of about 5 mL. The concentrate was lyophilized for 72 h to obtain the QRT-PHM. SV was dissolved in distilled water utilizing the SV amounts specified in the design. The SV aqueous solution was used as the hydration medium for the dried QRT-PHM preparation to prepare the QRT-PHM-SV nanovesicles, that was then stored in airtight amber colored glass containers at 4 °C until further use. Plain PHM-SV was prepared with the same procedure mentioned in this section, but without the use of QRT.QRT-PHM-SV formulations were prepared using reflux followed by anti-solvent precipitation, as described before with some modification . BrieflyQRT-PHM-SV size and zeta potential were determined by appropriate dilution in double-distilled water using a Zetasizer Nano ZSP particle size analyzer instrument . The results were expressed as the average of five determinations. The parameters were the following: laser wavelength of 633 nm, scattering angle of 173, temperature of 25 °C, medium viscosity of 0.8872 cP, and medium refractive index of 1.33.The examined independent variables were optimized by a numerical method following the desirability function approach. The optimization aimed primarily at minimizing the size, and maximizing the absolute zeta potential of the proposed QRT-PHM-SV formulation. Nonetheless, the predicted optimized formulation was prepared for further characterization.The optimized QRT-PHM-SV formulation was investigated utilizing JEOL GEM-1010  transmission electron microscope (TEM) at 80 kV at the Regional Center for Mycology and Biotechnology (RCMB) Al-Azhar University, Cairo, Egypt. One drop of the sample was spread on a carbon-coated grid, dried, and then 1% phosphotungistic acid was used for negative staining of the sample. The sample was then dried at ambient temperature for 15 min before visualization.3 cells per well, and incubated overnight. After stabilization, cells were treated with plain PHM-SV, QRT-raw, and QRT-PHM-SV and incubated for 24 h. Then, previously treated cells were further treated with 5.0 mg/mL MTT solution (10 µL), and then incubated for 4 h at 37 °C. Additionally, the collected supernatant was dispersed in 100 µL of DMSO to solubilize the formazan crystal. Samples were analyzed employing a microplate reader at 570 nm. Studies were carried out in triplicate. The half-maximal inhibitory concentration (IC50) for MCF-7 cells was determined for cells treated with plain PHM-SV, QRT-raw, or QRT-PHM-SV. The IC50 values were estimated after plotting the percent of the variation in cell viability versus drug concentration.The cytotoxicity efficacy of optimized QRT-PHM-SV was performed on the MCF-7 cell line using MTT assay. For this experiment, selected cells were grown in 96-well plates at the density of 5 × 10To analyze the effects of samples on the cell cycle, the flow cytometry method was utilized. The cells were treated with various sample formulations: plain PHM-SV, QRT-raw, and QRT-PHM-SV, and incubated for 24 h. After completion of incubation, cells were separated by centrifugation and fixed with 70% cold ethanol. Prior to the washing of samples with PBS, samples were again collected by centrifugation. Separated cells were stained with propidium iodide supplemented with RNAse before starting flow cytometry analysis .5 cells per well, then incubated overnight with IC50 concentration of samples for 24 h at 37 °C. All samples were then centrifuged at 200× g for 5 min, and collected cells were resuspended in PBS at room temperature after dual washing. Furthermore, 10 µL Annexin V and 5 µL propidium iodide solution supernatant was dispersed in the previously prepared samples and incubated at 25 °C for 5 min. Final samples were analyzed using a flow cytometer  in triplicate [In order to analyze the comparative apoptotic activity of plain PHM-SV, QRT-raw, and QRT-PHM-SV, the Annexin V method was implemented. For this purpose, selected cells were grown in 6-well plates at the density of 1 × 106 cells per well and treated with plain PHM-SV, QRT-raw, and QRT-PHM-SV. Then samples were resuspended in ice-chilled lysate buffer and incubated in an ice medium for 10 min before centrifugation . The analysis method for the Caspase 9 assay was carried out according to the instructions of the manufacturer, and the developed color was determined by a microplate reader at 405 nm.The Caspase 9 determination was carried out through the Caspase 9 Colorimetric Assay Kit . In this case, MCF-7 cells were grown in the density of 3 × 10® Bcl-2 ELISA Kit, 24 h after drug/phytosomal formulations treatment according to the instructions of their manufacturers. The untreated cells were used as a negative control.Bax and Bcl-2 proteins were quantified via their corresponding ELISA kits, Bax ELISA kit  and ZymedMCF-7 cells were incubated with drug/phytosomal formulations after being cultured in 96-well plate at a density of 1 × 105 cells/well. After that, Mitochondrial membrane potential (MMP) changes were investigated via the use of MitoProbe™ TMRM Assay Kit. The untreated cells were used as a negative control.Real-time polymerase chain reaction (RT-PCR) for estimation of p53 and TNF-α:The expression of p53 and TNF-α was determined by using RT-PCR. MCF-7 cells were treated with the different formulas incubated. Total RNA was extracted from the cell fraction and then from the synthesis of cDNA. Primers for p53 and TNF-α were designed using Gene Runner software. Samples in triplicates were used to estimate the expression, and they were normalized to β actin.Western Blot investigation of Caspase 9, Bax, Bcl-2 and p 53 proteins expression:Western blot assay of plain PHM-SV, QRT-raw, and QRT-PHM-SV was performed utilizing the previously reported protocol and by our laboratory ,26. The p-value < 0.05 was considered significant.Values were expressed as mean ± standard deviation (SD). One way ANOVA, followed by Tukey’s multiple comparison test, was utilized for statistical analysis in which 2 and lowest PRESS. The adjusted R2 and the predicted R2 showed good coincidence with a difference of less than 0.2. Moreover, both responses showed adequate precision values greater than the desirable value  indicating an appropriate signal to noise ratio. Thus, the quadratic model could be considered adequate to navigate the experimental design space for both responses.Fit statistical analysis results for vesicle size and zeta potential are summarized in Diagnostic plots were constructed for establishing the goodness of fit of the selected model for both responses, as illustrated in The restricted penetration of drug delivery systems into cancerous tissues represent a major obstacle in the field of cancer therapy, as this lessens their clinical efficacy. Thus, continuous research is being conducted to modify the physicochemical properties of drug delivery systems to target tumor penetration .Lipid-based delivery systems have recently attracted attention in the field of cancer therapy. A particle size smaller than 400 nm was reported for preferential distribution within solid malignant tissues ,31. AccoZeta potential is an indication of the surface charge of the nanoparticulate systems. It is reported that cationic nanoparticulate systems could exhibit a significantly increased permeation into cancerous cells and accumulate in tumor tissue and tumor vasculature, compared with the surrounding tissue ,33,34. Ap < 0.0001), respectively. The lack of fit F-value of 0.33 (p = 0.8854) for vesicle size and 3.92 (p = 0.2201) for zeta potential indicated non-significant lack of fit in relation to pure error, thereby assuring data fitting to the selected model. The software was employed to generate the equations demonstrating the quadratic model for both responses in terms of coded factor, as follows:Analysis of variance (ANOVA) for both vesicle size and zeta potential confirmed the significance of the quadratic model, as depicted by the corresponding F-values of 44.16 and 11.37 . The interaction term X2X4 corresponds to the interaction between process temperature and SV amount, in addition to the quadratic terms X12, X22, and X42, which were also found to be significant at 95% significance level. Regarding zeta potential, the linear terms X1 (p = 0.0001), and X4 (p = 0.0001) were significant, in addition to the interaction term X1X4 (p = 0.0029), and the quadratic terms X12 (p = 0.0005) and X22 (p = 0.0204) were significant. The statistical analysis revealed that the linear terms X® was implemented for predicting the optimized levels of each studied independent variable by a numerical optimization technique. The optimized levels that could yield minimized vesicle size and maximized zeta potential upon combination with a desirability of 0.996 are presented in Design Expert softwareThe results of the TEM investigation for the optimized formula showed spherical vesicles . The sizQRT-PHM-SV formulation showed potent cytotoxicity to MCF-7 cells:50 value of the QRT-PHM-SV formulation was determined based on the cytotoxic activity of the applied drug or drug formulations on MCF-7 cells. The plain formula, PHM-SV, induced very limited cytotoxicity to MCF-7 cells, as reflected by its high IC50 value  increase in Bax expression of QRT-PHM-SV formula when compared with the investigated groups: untreated control, QRT, and PHM-SV (p < 0.05) increase in the expression for the QRT-PHM-SV formula when compared with untreated control and QRT.To investigate the effect of QRT-PHM-SV formula on Bax, Bcl2, P53 and Casp9, western blot assay was carried out . The resd PHM-SV A,B. The KB is known to inhibit apoptosis, particularly that triggered by TNF-α [KB activation was reduced upon treatment with PHM-SV and PHM-QRT-SV to about 0.4-fold of the control value. Treatment with free QRT reduced NF-KB activation to about 0.5-fold relative to the control. Taken together, the increased TNFα expression and reduced NF-KB activation associated with QRT-PHM-SV confirm the obtained results highlighting the superior activity of QRT-PHM-SV to induce cell apoptosis when compared with the plain formula.TNF-α is a cytokine that can induce apoptosis in a variety of tissues and cell types including breast cancer cells . On the by TNF-α . In this1 and X4 coefficients and the negative sign the X2 coefficient supports this observation. In addition, the PL amount was the most significant factor affecting the vesicle size, as evidenced by the highest coefficient of its corresponding linear term. In general, the increase in size with increasing PL amount observed in our study was consistent with the results reported for previously developed vesicular systems [Phytosomes, when compared with liposomes, enhance pharmacokinetic properties and hence improve the bioavailability of most botanicals ,7. The i systems ,41. For  systems . Moreove systems  reported systems  reported systems ,44. Conc systems ,46.The findings from this study have significant practical applications. It has been previously shown that QRT induces apoptosis in breast cancer cells, and it also has the potential to work synergistically with the cytotoxicity of conventional cytostatic agents . PreviouThe developed formula, QRT-PHM-SV, exhibited a significantly larger cell population in the S, G2-M and pre-G1 phases demonstrating an enhancement in the cytotoxicity of QRT by the developed formula. A characteristic feature of apoptosis is a high cell fraction at the pre-G1 phase. Hence, the induction of cell-cycle arrest could provide an explanation for the improved cytotoxic activity of the QRT-SV-phytosomes. This is in agreement with other reports on nanoparticles . ApoptosAn excellent marker of apoptosis is caspase, as it is a cell death protease that regulates the breakdown of DNA and cellular proteins in apoptotic cells . Thus, tKB activation. This is in agreement with the enhanced cytotoxic and proapoptotic activities of the QRT-PHM-SV. TNF-α is known to promote apoptosis in breast cancer cells, whereas NF-KB can inhibit apoptosis triggered by TNF-α [Furthermore, QRT-PHM-SV-treated cells demonstrated an increased TNF-α expression and a decreased NF-by TNF-α . Hence, The experimental design was used in this study to formulate and optimize QRT-SV phytosomes. The prepared formulations were nano-sized, and had a high zeta potential. The optimized QRT-SV phytosomes revealed that QRT formula administration significantly boosted caspase-9, Bax, Bcl-2, and p53 mRNA expression, as compared with the plain formula and QRT. In terms of inflammatory indicators, the QRT formula dramatically reduced TNF- and NF-B activity when compared with the basic formula and QRT alone. Overall, the study’s findings demonstrated that the developed optimized QRT formulation could be a promising therapeutic method for the treatment of breast cancer.


O-linked N-acetylglucosamine (GlcNAc). Just like neoantigens these modified antigens may have specific immunomodulatory functions. Here we compiled from literature, and a new dataset originating from the JY B cell lymphoblastoid cell line, a concise albeit comprehensive list of O-GlcNAcylated HLA class I peptides. This cumulative list of O-GlcNAcylated HLA peptides were derived from normal and cancerous origin, as well as tissue specimen. Remarkably, the overlap in detected O-GlcNAcylated HLA peptides as well as their source proteins is strikingly high. Most of the O-GlcNAcylated HLA peptides originate from nuclear proteins, notably transcription factors. From this list, we extract that O-GlcNAcylated HLA Class I peptides are preferentially presented by the HLA-B*07:02 allele. This allele loads peptides with a Proline residue anchor at position 2, and features a binding groove that can accommodate well the recently proposed consensus sequence for O-GlcNAcylation, P(V/A/T/S)g(S/T), essentially explaining why HLA-B*07:02 is a favoured binding allele. The observations drawn from the compiled list, may assist in the prediction of novel O-GlcNAcylated HLA antigens, which will be best presented by patients harbouring HLA-B*07:02 or related alleles that use Proline as anchoring residue.Mass-spectrometry based immunopeptidomics has provided unprecedented insights into antigen presentation, not only charting an enormous ligandome of self-antigens, but also cancer neoantigens and peptide antigens harbouring post-translational modifications. Here we concentrate on the latter, focusing on the small subset of HLA Class I peptides (less than 1%) that has been observed to be post-translationally modified (PTM) by a  Peptide antigen presentation by the human leukocyte antigen (HLA) to T-cells forms an integral part of the human immune surveillance . CharactO-GlcNAc) is known to engage in reciprocal crosstalk with phosphorylation  reside. When O-GlcNAcylated proteins are degraded via the proteasomal route and loaded onto the HLA Class I molecules, the presented peptides can retain the O-GlcNAc moiety from the source protein. Notably, aberrant O-GlcNAcylation has been shown to correlate with augmented cancer cell proliferation, survival, invasion, and metastasis , and fragment less efficiently into complementary series of feasible , 24. Usipeptides , 26. BotO-GlcNAc HLA Class I peptides presented by the non-cancerous JY B-lymphoblastoid cell line. We compared this dataset with the earlier reported O-GlcNAc HLA Class I peptides described in the literature to collate a total list of 55 O-GlcNAcylated HLA Class I peptides. Our compilation includes the glycopeptide sequence, the likely subcellular localisation of the proteins of origin and the predicted HLA allele and their respective binding affinity. We observed substantial congruence between the glycopeptide sequences we report here and the existing datasets of Malaker et al, and Marino et al. While comparing these datasets we noticed that O-GlcNAc HLA Class I peptides were presented with a marked preference by HLA-B*07:02, which harbors a Pro anchoring site for the P2 position.In this report, using hybrid fragmentation MS strategies, we identified a new set of in total 23 via immunoaffinity purification, as described previously  was cultured in RPMI 1640 supplemented with 10% fetal bovine serum, 50 U/mL penicillin, and 50 μg/mL streptomycin. HLA class I peptides were retrieved eviously , 27. Speeviously , and antm/z 350 to 1400 were acquired at a resolution of 120,000 in the Orbitrap after accumulation to a target value of 4 × 105 or a maximum injection time of 50 ms.The HLA Class I peptides were analyzed using an Ultimate 3000 UHPLC (Thermo Fisher Scientific) coupled to an Orbitrap Fusion Lumos (Thermo Fischer Scientific). The peptides were trapped on  for 5 min in solvent A (0.1% formic acid in water) before being separated on an analytical column . Solvent B consisted of 80% acetonitrile in 0.1% formic acid. The gradient was as follows: first 5 min of trapping, followed by 100 min gradient from 5% to 40% solvent B. Subsequently, 10 min of washing with 99% solvent B and 10 min re-equilibration with 9% solvent A. The mass spectrometer operated in data-dependent mode. Full scan MS spectra from m/z 120 were chosen for fragmentation using the MIPS algorithm with 1.2 Da isolation window. The fragmentation was performed using 30% normalized collision energy (NCE) on selected precursors with 30s dynamic exclusion after accumulation of 5×104 ions. Focused characterisation of the O-GlcNAcylated immunopeptidome was performed by electron transfer dissociation (ETD) and energy-stepped HCD, triggered when two of the six O-GlcNAc signature fragment ions  were detected at > 5% relative abundance, as described previously  MS/MS spectra were acquired at a resolution of 60,000. Charge states of 2 up to 4 starting at eviously , 30.O-GlcNAcylation. Peptides were considered binders when % Rank < 2. We classified the binding predictions for the O-GlcNAc HLA peptides as strong (IC50 ≤ 50 nM), regular (50 nM < IC50 ≤ 500 nM) and weak (500 nM < IC50 ≤ 5000 nM) binders for peptide sequence. Gene ontology (GO) analysis of the source proteins of the O-GlcNAcylated HLA class I peptides was performed by functional annotation tool DAVID  edited with the JY-specific HLA proteins and 20 most abundant FBS contaminants, with no enzyme specificity in the search engine to identify the unmodified HLA ligandome. Precursor ion and MS/MS tolerances were set to 10 ppm and 0.03 Da. Methionine oxidation and cysteinylation were set as variable modifications. Peptides were filtered by precursor tolerance 5 ppm, < 1% FDR, XCorr > 1.7, and peptide rank 1. Only peptides between 8 and 12 amino acid long were selected for further analysis. Glycopeptide assignments were made by searching the same RAW files using Byonic 4.1. Precursor and MS/MS tolerance were set to 10 ppm and 0.03 Da. Peptide loading affinity was predicted with NetMHC 4.1 algorithm for the identified HLA peptide sequences against HLA-A*02:01, HLA-B*07:02 and HLA-C*07:02 allele, without considering potential changes in loading affinity due to ol DAVID . The mashe PRIDE  partner O-GlcNAcylated HLA class I peptides. In our calculation we count each unique peptide just once, even when it is identified with multiple different PTMs. To boost the identifications we examined the occurrence of diagnostic oxonium ions  in the MS2 spectra, and used these features to trigger targeted analysis by ETD and stepped HCD, to boost the generation of fragment ions for identification of the glycopeptide backbone  were predicted to be strong binders of the HLA-B*07:02 allele , from the GR B-lymphoblastoid cell line  were found by them exclusively in the leukemia samples, and they reported solely one O-GlcNAcylated HLA peptide detected in the JY cells (as opposed to the 23 we report here). This increase in identifications of O-GlcNAcylated HLA Class-I peptides, when compared to the study of Malaker et al., could be due to multiple issues, such as the use of a more sensitive mass spectrometer, refined bioinformatics tools and a combination of hybrid fragmentation triggering methods. Moreover, Malaker et al. studied the JY cells just as a model system, focusing more on the primary cancer cells and tissues. As in the work of Marino et al., several (10 out of 34) O-GlcNAc peptides were also detected to carry one or more monosaccharide extensions .Malaker et al. analyzed the HLA class I immunopeptidomes from blood cells of primary leukemia  patients, relying on a HLA-B*07-specific antibody (ME1) to pull down HLA Class I complexes . HealthyO-GlcNAcylated HLA class I peptides detected in each of these studies, as well as in the current study, we had expected a priori that such small number of identifications might render the overlap in between these studies to be close to zero. Nonetheless, this was not the case, and instead, there was a substantial overlap in detected O-GlcNAcylated HLA class I peptides  peptides are predicted to be strong binders to HLA-B*07:02 allele. This finding was expected for the dataset of Malaker et al. as they used an antibody specific for HLA-B*07, but in Marino et al. and in the current study a pan-HLA antibody was used that has no specific preference for the HLA-B*07 allele. Thus, the compiled data hint at an overall preference for O-GlcNAcylated HLA class I peptides to be presented by HLA-B*07:02, regardless of the immunopeptidome isolation strategy.One of the most striking features examining all identified O-GlcNAcylated peptides by the HLA-B*07:02 allele, we evaluated the peptide amino acid sequences of the 55 O-GlcNAcylated HLA peptides and compared them against the sequences of the unmodified peptides observed to bind the HLA-B*07:02 allele in the current dataset. Considering the site of modification, most O-GlcNAc sites were found in between position 3 to position 7 (P3-P7), with the highest incidence at position 4 (P4). Peptides presented by HLA-B*07:02 have a high preference for a proline residue at P2 position (O-GlcNAcylated peptides (32 peptides in the compiled list) predominantly confirmed the presence of a proline residues at P2 along with preference for a valine residue at P3, a serine at P4  and a serine or threonine residue at P5  as illustrated in O-GlcNAcylated peptides (12 peptides) were also rather consistent with a proline residue at P2 or P3, valine in P4 and serine/threonine residues at P4 to P6 -g(S/T)-(S/T)-A -A . This moO-GlcNAcylated HLA class I peptides from three recent studies, the substantial overlap in peptides detected in all or at least two of these studies was striking. Moreover, some other peculiar features were also repeatedly observed in all studies, for instance, the frequent co-occurrence of other PTMs, and the observation of glycan extensions next to the O-GlcNAcylation.Compiling the combined dataset of O-GlcNAcylated HLA peptides. For instance, the most immunogenic O-GlcNAcylated peptide identified by Malaker et al. was me-RPPIgTQSSL, originating from the RNA-binding protein 27 (aa 382–390), which contains both a methylated N-terminal arginine and an O-GlcNAcylated threonine at P5. Interestingly, killing of autologous B cells pulsed with methylated, O-GlcNAcylated, and the doubly modified HLA peptide was observed in their study, however not with the unmodified peptide. Potent T-cell response with the doubly modified peptide suggests that methylation may potentially further boost immunogenicity. This HLA peptide was detected in quite a few different peptidoforms in their studies O-GlcNAcylated peptides from the RNA-binding protein 27, albeit all within the N-terminal extended backbone IPRPPIgTQSSL (aa 380–390) O-GlcNAc, one GlcNAc (Thr5), hexose-GlcNAc and even more extended glycans with mass shifts of 730 and 1021 Da. Moreover, arginine (Arg3) was observed to be modified with a mono-methyl (Arg3) or asymmetric di-methyl (Arg3), both with and without the glycan modifications. Thus, although we define RPPgTQSSL and IPRPPIgTQSSL as one unique peptide in Both Marino et al. and Malakar et al. observed glycan extensions on quite a few O-GlcNAc on Ser891. These peptides were also detected with mass extensions of 0, 203, 365, 656, 730, and 1021 Da, all indicative of a peptide backbone with extensions from 0 up to 6 glycan moieties. Both PTM sites have been reported previously, although no report is known if they co-occur in the source protein   protein , 41. TheO-GlcNAcylated form.Finally, Malaker et al. reported glycosylation on the peptide IPVSSHNSL from the Myocyte-specific enhancer factor 2C protein, a transcription factor implicated in leukemogenesis , that waO-GlcNAcylated RPPVgTKASSF in complex with HLA molecule by Marino et al. revealed the glycan moiety was solvent exposed and not directly involved in the binding to the HLA Class I molecule groove. It is therefore plausible, as suggested earlier by Marino et al., that they may be first formed by proteasomal degradation as O-GlcNAcylated peptide, and then loaded onto an HLA class I molecule and extended by glycosyltransferases, starting with β1, 4 galactosyltransferase, in the Golgi, but this needs further validation. In general, the co-occurrence of so many peptidoforms of O-GlcNAcylated HLA Class I peptides raises the question whether all these forms have specific T-cell response and whether they are differentially immunogenic.The origin of these additional glycan moieties is still puzzling. Most of the proteins observed to be decorated with these glycan extensions find their origin in the nucleus. Computational modelling of O-GlcNAcylated HLA peptides from JY cells, with two recent related studies, we were able to compile a concise list of bone fide O-GlcNAcylated HLA peptides, presented by a whole array of different cancer and non-cancerous cells. Our comprehensive analysis of the sequences of all these peptides revealed preferential presentation of O-GlcNAcylated HLA peptides by the HLA-B*07 allele. The proline at P2 position of this allele allows the semi-consensus sequence motif of O-GlcNAcylation to be presented prominently. This preferred rule of presentation by HLA-B*07 and HLA-B*07-like alleles may also be useful in predicting putative O-GlcNAcylated HLA peptide antigens. Moreover, although the number of O-GlcNAcylated HLA peptides detected are small in each of the three studies evaluated, the overlap between these diverse studies were substantially high. Several of the reported O-GlcNAcylated HLA peptides are presented in multiple peptidoforms, carrying either additional phosphorylation or arginine-(di)methylation, along with glycan extensions of between 0 to 6 more carbohydrate moieties. Whether such distinct peptidoforms have differential functionality in immune surveillance needs to be further addressed.Combining data from our new dataset of www.ebi.ac.uk/pride, PXD028874.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: AH conceived the idea. AS-B performed the MS experiments. SM and AS-B analyzed the data and wrote the initial draft of the manuscript along with AH. SM and AS-B contributed equally to this manuscript. All the authors contributed to the article and approved on the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To solve swine feces impurity interference, by adding a filtration unit design of LFIA without an additional pretreatment procedure, the LBs-LFIA gave good agreement (92.59%) with RT-PCR results in the analysis of clinical swine fecal samples (n = 108), which was more accurate than previously reported colloidal gold LFIA (74.07%) and fluorescent LFIA (86.67%). Moreover, LBs-LFIA showed sufficient accuracy (coefficient of variance [CV] < 15%) and stable (room temperature storage life > 56 days) performance for PEDV detection, which is promising for on-site analysis and user-driven testing in pig production system.Porcine epidemic diarrhea virus (PEDV), as the main causative pathogen of viral diarrhea in pigs, has been reported to result in high morbidity and mortality in neonatal piglets and cause significant economic losses to the swine industry. Rapid diagnosis methods are essential for preventing outbreaks and transmission of this disease. In this study, a paper-based lateral flow immunoassay for the rapid diagnosis of PEDV in swine fecal samples was developed using stable color-rich latex beads as the label. Under optimal conditions, the newly developed latex bead-based lateral flow immunoassay (LBs-LFIA) attained a limit of detection (LOD) as low as 10The online version contains supplementary material available at 10.1186/s44149-021-00029-1. Alphacoronavirus in the family Coronaviridae, causes watery diarrhea, vomiting and dehydration, and has 100% morbidity and approximately 80-100% mortality in neonatal piglets , enzyme-linked immunosorbent assay (ELISA), quantitative real-time-reverse transcription-polymerase chain reaction (qRT-PCR), conventional RT-PCR, reverse transcription loop-mediated isothermal amplification (RT-LAMP), and reverse transcription recombinase polymerase amplification assay (RT-RPA) , an ideal platform for performing immunoassays in a low-cost, easy-to-use manner, have widespread use for on-site screening of diseases in nonlaboratory settings and self-testing by inexperienced pig farmers  and a Malvern laser particle size analyzer. As shown in Fig. Subsequently, to determine the mass of antibodies conjugated with each LB, supernatant from the LB-mAb reaction system were analyzed. BCA assay results showed that the concentration of protein in the supernatant from the LBs was 2.55 ± 1.29 μg, and the labeling efficiency of antibodies on the LBs was at least 91.46%, indicating that most of antibody proteins were conjugated to the LBs Table . From thIt is known that the most challenging and complicated process during the development of LFIAs is the tuning of the different parameters and components to achieve a minimum amount or concentration that could be detected. Optimization of the developed LFIA relied on an iterative approach using trial and error . As shown in Fig. 2.75–106.35 TCID50/mL, 9.77–40,000 ng/mL) and the negative control were added to the sample pad hole. With an increase in PEDV concentration, T line on the LBs-LFIA appeared as a clear red band. The color intensity of T line, calculated from the gray value, was extracted from the image to realize semiquantitative analysis. The gray value of T line exhibited a linear relationship with the PEDV virus titer in the range of 103.65 to 106.35 TCID50/mL . The color of red band on T line was completely absent when testing 103.95 TCID50/mL of PEDV , porcine reproductive and respiratory syndrome virus (PRRSV), classical swine fever virus (CSFV) and porcine circovirus (PCV) were selected as controls. Images were captured in daylight using a smartphone camera, and the gray values of T line color intensity were analyzed using ImageJ software Fig. b. As shoIn the present study, stability of the LBs-LFIA to high and PEDV oncentrations and the negative control was tested after the device was stored for 7, 14, 23 and 56 consecutive days. As shown in Fig. To explore the potential use of this LB-LFIA for PEDV detection, we evaluated the clinical feasibility of this platform for the selection of swine fecal samples confirmed by RT-PCR to have 70 PEDV-positive samples and 38 PEDV-negative samples. As shown in Fig. P < 0.001). In conclusion, in the clinical sample analysis, the developed LBs-LFIA for POC diagnosis of clinical swine fecal samples showed great potential for monitoring PEDV infection in the field.Of the 108 swine fecal samples, all negative RT-PCR results were negative in the LBs-LFIA. Eight swine fecal samples that were positive by RT-PCR were negative in the LBs-LFIA, and the remaining samples were positive by both RT-PCR and in the LBs-LFIA Fig. . Cohen’s3.60TCID50/mL), no cross-reactions with other pathogens, and qualified stability.Effective diagnosis of animal pathogens requires rapid, sensitive and specific techniques that can be used for routine diagnosis in the laboratory and in the field. In recent years, LFIA has been widely used in clinic because of its rapid detection, easy operation, and cost-effectiveness. In our study, a paper-based lateral flow immunoassay for PEDV detection in swine fecal samples was developed using color-rich latex beads as the label. The analytical performance showed high sensitivity . LBs, N-(3-dimethyaminopropy)-N′-ethylcarbodiimide hydrochloride (EDC), and N-hydroxysulfosuccinimide (sulfo-NHS) were purchased from Thermo Fisher Scientific . 2-(N-morpholino) ethanesulfonic acid (MES) and bovine serum albumin (BSA) were purchased from Sigma-Aldrich . Goat anti-mouse IgG was purchased from Artron . Nitrocellulose (NC) membranes, sample pads, filter pads, conjugation pads, plastic backing, and absorbent pads were purchased from Shanghai JieNing Biotech Co., Ltd. . Ultrapure water was produced by a Milli-Q Ultra Pure System  and used throughout the study. All chemicals were of analytical grade or higher.A centrifuge , XW-80 vortex mixer , ultrasonic homogenizer , programmable strip cutting machine HGS201 , XYZ3060 platform , and an electric forced air convection drying oven  were used to produce the LFIA. A transmission electron microscope  and a particle size analyzer  were used to characterize the LBs.w/v) EDC and 1% (w/v) sulfo-NHS solution were successively added to 0.2% (w/v) LB solution in a 1 to 5 ratio. After reacting on a rotary mixer for 30 min at room temperature, the activated LBs were separated by centrifugation at 20,000 g for 30 min at 4 °C and resuspended in 0.1 M MES buffer by sonication to assist in the redispersion of the clumped particles. Subsequently, 20 μg of anti-PEDV monoclonal antibodies, as the detecting mAbs, were added to the activated LB solution, and the reaction tube was placed on a rotary mixer for 90 min. Then, 10 μL of 10% (w/v) BSA solution was added to 1 mL of binding reaction system to block nonspecific binding, and the solution was rotated gently for 60 min at room temperature. Finally, the solution containing antibody-conjugated LBs was separated by centrifugation at 20,000 g for 30 min at 4 °C. The precipitate was resuspended in a half volume of glycine-NaOH buffer  containing 8% (w/v) sucrose, 2% (w/v) trehalose, 1% (w/v) BSA, 0.5% (w/v) sodium casein, and 0.02% (w/v) sodium azide, and the particles were dispersed in solution by ultrasonication.The color-rich dyed latex beads were conjugated with anti-PEDV-monoclonal antibodies by heterobifunctional cross-linking using EDC and sulfo-NHS, which preactivated the carboxyl groups on the particles to allow easy binding to the free amino group of the antibodies to form stable amide bonds. Before the binding reaction was started, 1% (w/v) sucrose, 1% (v/v) Tween-20, and 0.05% (v/v) ProClinTM300 and dried at room temperature for 10 h. A piece of the NC membrane  was used to immobilize 0.75 mg/mL anti-PEDV-capturing monoclonal antibodies and 3 mg/mL goat anti-mouse IgG polyclonal antibodies in distinct zones with a volume of 0.5 μL/cm using a dispenser and then dried at 37 °C overnight. Each LB-LFIA was assembled by superposition of the different pads and then was cut into 3.8 mm wide single-use strips that were housed in a cassette and stored in bags containing desiccant until use.To prepare the LFIA strips, the pretreated sample pad, filtration pad, conjugate pad, NC membrane, and absorbent pad were deposited onto a polyvinyl chloride card with an overlap of 1–2 mm in length. The sample pad was soaked with phosphate buffer  containing 2.5% (via capillary action through the test line (T line) and control line (C line) on the NC membrane. After reacting for 15 min, the gray value of the red band on T line that reflects the virus concentration was recorded by a smartphone camera and analyzed by ImageJ software.The following assay procedure was used for the LBs-LFIA: 100 μL of running buffer was mixed with the 1/2 swab (appropriate 0.5 g) sample of pig feces, and then the supernatant was transferred to the sample pad of the LFIA. Upon addition of a liquid sample, the particles on the conjugate pad were rehydrated, and the antigen (if present) and the sample migrate The interpretation time of the LBs-LFIA, the amount of mAbs labeled with LBs, the concentration of capturing antibodies on T line, and the pH of the running buffer were considered critical parameters governing the analytical performance of the developed LBs-LFIA. Following optimization, we investigated the application of the LBs-LFIA for the analysis of swine fecal samples and addressed the problems impairing the performance of the LBs-LFIA by optimizing the running buffer and adding the filter pad.A single-factor analysis was applied to optimize the parameters in this study. The details of the condition optimization of the LFIA system are provided in Table To evaluate the analytical sensitivity of the LBs-LFIA, different known concentrations of PEDV were spiked in sample diluent mixed with appropriate PEDV-negative swine feces, and serial dilutions of the starting sample were prepared. After mixing the diluent, 100 μL of the supernatant mixture was added to the sample pad hole. Each concentration was analyzed in triplicate. The results were diagnosed by the naked eye, as the colorimetric change provided suitable visual analytical sensitivity. Images of T line captured with a smartphone camera were also processed to obtain the corresponding gray values to quantify the analytical performance of the LBs-LFIA. The standard curve was generated from a known amount of virus, and the gray values of T line were used to determine the analytical sensitivity. Moreover, after determining LOD of the LBs-LFIA, we further verified LOD with different PEDV strains. The specificity of the LBs-LFIA was determined by analysis of different common swine viruses prepared at the same concentration. The accuracy was determined by replicate analysis of LBs-LFIAs that contained known PEDV concentrations, with the measurement of five determinations for each analyte concentration.The stability of an LB-LFIA product is a critical factor in its suitability in research and development (R&D). In this study, to estimate the storage stability of LBs-LFIA, the same batch of LBs-LFIA was divided into three subgroups, which were stored separately at room temperature , 37 °C and 50 °C. The LB-LFIAs were evaluated for the desired performance using actual storage condition tests and accelerated aging studies. Before beginning the shelf-life evaluation, the LB-LFIA strips were packed in aluminum foil bags with desiccant. Fifteen pouches were tested on the first day. Forty-five pouches were each maintained at 37 °C and 50 °C in a drying oven, and 45 pouches were stored at RT for 56 consecutive days. Pouches were taken from each of the storage conditions after 7, 14, 23 and 56 d, and the LB-LFIA strips were used to analyze a high concentration of PEDV, a low concentration of PEDV, and a negative control. Five replicates were performed. The qualitative judgment standard was based on the presence of a red band on T line.To verify the feasibility and practicability, 108 swine fecal samples were analyzed for the presence of PEDV using the LBs-LFIA. The presence of PEDV in the swine fecal samples was first confirmed by RT-PCR, which is defined as the gold standard for PEDV detection. As illustrated in Fig. Additional file 1.


Alteration in salivary composition and its effect on the oral cavity in diabetic child patients remains equivocal. This study was performed to assess the relationship between salivary factors and gingival status in children with type-1 diabetes mellitus (DM). In this cross-sectional study, 120 subjects aged 6-16 years were examined to determine the gingival index (GI) and plaque index (PI). The unstimulated saliva samples were collected to measure thesalivary triglyceride, cholesterol, albumin, α-amylase, total protein levels by the laboratory kits. Total antioxidant capacity and thefree radicals scavenger index were measured by the Ferric Reducing Ability Of Plasma (FRAP) and 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) assays,respectively. Data were analyzed by parametric and non-parametric, Pearson correlation, and t tests at a 5% error level.p< 0.001).No significant difference was found between the PI of diabetics compared to healthy volunteers .The levels of salivary triglyceride and cholesterol, albumin and total proteins in healthy subjects were significantly higher than that in people with DM(p< 0.001). A significantly more salivary α-amylase activity was found in diabetics compared to non-diabetics (p< 0.001).No significant differences were found between diabetic and non-diabetic subjects in terms of DPPH  and FRAP, respectively). GI of diabetics was significantly higher than that of healthy individuals (1.51± 0.71 and 0.9±0.81, respectively,  More gingival inflammation and salivary α-amylase activity and lower level of salivary lipids, albumin, and total proteins werefound in diabetic patients, but there was no association between the level of lipids, proteins, and the total antioxidant capacityof saliva with periodontal health indicators in patients with DM and healthy individuals. Diabetes mellitus (DM) is a group of metabolic disorders with serious complications reducing the quality of life - 2.It iPeriodontal diseases and gingivitis were reported as the sixth most common complication of diabetes, which are usuallyassociated with the severity of disease  - 6.SomeThe use of saliva instead of serum has recently been preferred as a diagnostic medium. Saliva offers advantages over bloodbecause it is a cost-effective and non-invasive method that can be collected by persons with modest education . One appAccumulation of reactive oxygen species, oxidative stress, and interactions between advanced glycation end products(AGEs) in the periodontal tissues and their receptor (RAGE) all contribute to increased inflammation in the periodontaltissues in people with DM  .There iSalivary albumin is regarded as a serum ultrafiltrate to the mouth, and it may diffuse into the mucosal secretions.Hormonal balance, nutrition, and osmotic pressure regulate albumin synthesis.High concentrations of salivary albumin have been detected in a medically compromised condition, such as immunosuppression and DM.Both normal and raised salivary albumin levels have been seen in periodontitis - 17. In association with α -amylase, some studies have suggested that this salivary enzyme contributes tothe microorganisms’ adhesion and the microbial plaque formation; however, other studies have found thatα-amylase secretion is associated with a reduced risk of caries, a decrease in oral bacteria,and a reduced risk of periodontal disease  - 20.DM This cross-sectional study performed on 6- to 16-year-old diabetic and healthy volunteers with normal body mass index   22]. . 22]. Based on previous studies  - 24and The case group has consisted of patients who were diagnosed with type-1 DM by an endocrinologist at the Amirkola ChildrenHospital for at least three years. The quality of control of disease was determined based on the level of HbA1c.Patients with HbA1c more than 7.5% were considered into poorly-controlled DM group  .The patThe control group included healthy individuals who had not taken any medicine over the last month . They weThe study was approved by the Ethical Committee of the Research Council of Babol University of Medical Sciences (MUBABOL.REC.1395.55).The written consent was obtained from all subjects or their parents.Personal information and medical history of patients were obtained through interviewing participants/ parents and their medical records.In order to minimize the effect of the circadian rhythm, all saliva samples were collected from 10 to 11 AM, and then oralexamination was done. Unstimulated saliva samples were collected in disposable sterile tubes and immediately transferred tothe laboratory in a container containing dry ice at -4°C.The samples were centrifuged at 1500 g and 15 minutes, Clement 2000, North Sydney, Australia), the supernatant was collected intoEppendorf microtubes and were stored at -80°C until analyzes. Salivary cholesterol and triglyceride levels were assessed based on the colorimetric method using ZiestChem commercial kits according to the manufacturer's protocol  . In order to measure the α-amylase activity, 500µl of reagent was poured into the blank and the sample tubes and incubated at 37ºc for 5min.Then 20µl of the sample was added to the sample tube and incubated at 37ºc for 15min.Then immediately after that, the chemical reaction was stopped by adding 500 µl Iodine solution and 1500 µl distilled water.The absorption for the blank and the sample tubes was compared against distilled water at 405nm using UV-visiblespectrophotometer, and α-amylase activity was estimated by this equation  : Absorbance of blank-Absorbance of sample Absorbance of blank×1470= α-Amylase activityULSalivary total protein was measured in the Biuret method using ZiestChem commercial kits according to the manufacturer's protocol. Salivary albumin levels were measured based on colorimetric assay using the ZiestChemcommercial kit  also  - 30. Total antioxidant capacity (TAC) of salvia was measured according to Ferric Reducing Ability of Plasma (FRAP) assay,and the free radicals scavenger index was measured by DPPH assay (1.1-Diphenyl-2-picryl-hydrazyl) - 32.LöeData were statistically analyzed by Kruskal-Wallis followed by the Mann-Whitney U Test, One-way analysis of variance (ANOVA),Tukey Scheffe, t test, and Pearson correlation test in SPSS-22. All statistical tests were performed at the significancelevel of the p value less than 0.05.p< 0.001, The mean ages of the subjects in case and control groups were respectively 10.02±1.39 and 10.07±0.82 years. Thirty-three diabeticpatients (55%) were diagnosed with well-controlled DM (HbA1c less than 7.5%); approximately 4.45±2.43 years elapsed fromthe diagnosis of DM. The levels of salivary triglyceride and cholesterol, amylase, and GI in people with DM were significantlyhigher than that in healthy subjects  and femalesof non-diabetic subjects were significantly higher than those of diabetic subjects (p< 0.001).Salivary α-amylase activity in both males and females of the case group was significantly higher than thatof the control group (p< 0.001). Salivary albumin in diabetic men was significantly lower than thatof healthy ones (p<0.001).In terms of gender, the level of salivary lipids and total proteins in both males (p= 0.11).Nevertheless, no significant difference was found between the salivary albumin level of diabetic and non-diabetic females (This study aimed to evaluate the level of salivary lipids and proteins and TAC of patients with type-1 DM and their correlationwith gingival health status compared to healthy children. In the present study, a higher GI was found in diabetic patients comparedto non-diabetics. However, there was no significant difference in the PI of study groups. So, gingival inflammation in thesepatients seems not to be related to oral hygiene.et al. [ et al. [ Similar to this result, Alves et al.   indicate et al.   found no et al.  . Pathoge et al.  , changes et al.   and gene et al.  . A posit et al.  .They suIn the present study, the GI of poorly-controlled diabetic children was significantly higher than that of healthy children.Available literature data illustrated a correlation between the incidence and severity of gingival inflammationand poor metabolic control of DM  .So, conet al. [ Sadeghi et al.   showed aet al. [ Salivary cholesterol and triglyceride of diabetic patients were estimated lower than that of non-diabetic patients.In contrast, Priya et al.   found a et al.[ et al. [ In the present study, a higher level of salivary α-amylase was found in DM patients compared to healthy subjects.However, a higher level of salivary total proteins and albumin were observed in healthy individuals. Lakshmi et al.  showed a et al.   studied et al. [ et al. [ et al. [ et al. [ The TAC measured by FRAP and radical scavenger index determined by the DPPH assay were not significantly different among study groups.In contrast, Basir et al.  used the et al.   reported et al.  ,the mea et al.  estimateet al.[ In the present study, it was found no significant correlation between GI and PI and TAC. Aral et al.  reportedet al. [ In the study conducted by Reznick et al.  ,there wA higher GI was found in diabetic patients compared to healthy children, which was not related to microbial plaque accumulation.Salivary lipids and protein levels despite α-amylase in DM patients were lower than that of healthy subjects,but no difference was found between salivary lipids or protein levels in well-controlled and poorly-controlled patients.TAC of saliva was not significantly different between groups.No correlation was found between salivary lipids, proteins, and TAC with gingival health status.


Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets  for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy. Image understanding and Scene Recognition (SR) are keystones in computer vision. With the profusion of image and video datasets, robust software efficient techniques are crucial for data retrieval and processing . Human bUsing object detection and recognition in scene classification have drawn much attention in the last decade with object recognition aiming to mimic the human ability to identify and distinguish between multiple objects in images or video . Object Furthermore, researchers made various attempts in the SR task using Low-Level Image Features (SR-LLF), which aim to use low-level features including color, orientation, global multi-scale orientation, local dominant orientation (LDO), and texture to understand and classify scenes. The theory behind this algorithm relies on classifying the scene without the identifying said object. Most of the research attempts try to find descriptors that represent the low-level features and use these descriptors for the scene classification. As an extension for this approach, researchers employ methods for better selection of descriptors that enhance classification accuracy. Researchers that use this approach justify its preponderance over the Object Recognition (OR) method by the theory of error propagation, where errors in OR will lead to wrong classification of SR. Nevertheless, using low-level features  demonstrates a convenient performance, with a dramatic increase in the image complexity, leading to the successful implementation of robust and sophisticated models .Despite the optimal performance of existing models in image understanding and scene classification, there are still major issues. First, the training phase for the models necessitates a large amount of data, which is a difficult and time-consuming task. Furthermore, most models are reliant on data previously seen in the training set, resulting in ineffective models that can only identify samples that are similar to the training set.Meta-learning deciphers these limitations as it does not require a large number of training samples and it generalizes the model to be learnt and evaluated in novel classes as never seen before . Meta-leTo address the aforementioned issues, in the present work, two models are for image scenes classification. Those models were inspired by MobileNetV2 implementation . To builScene classification is a task that involves categorizing scenes from pictures. Objects or image descriptors are widely used in this task to achieve optimal accuracy. Unlike object classification which focuses on classifying influential objects in the foreground, objects are classified in SR based on their structure within the scene as well as the surrounding background. While humans can classify scenes in few seconds, researchers and computer engineers made vast attempts to make the computers mimic this ability.Deep learning (DL) and machine learning (ML) techniques have been employed for SR to optimize the performance of shallow learning techniques while also showing that DL and ML outperformed the current traditional learning techniques, achieving better accuracies .Machine learning and transfer learning are widely used on two benchmark scene datasets  showing significant results . ResNet-Various approaches and algorithms have been applied in FSL. The majority of algorithms used can be classified into three main classes. The first category is prior knowledge of similarity, in which models learn the hidden pattern of classification from training data. These patterns are used to classify classes that have never been seen before . The algorithms used in this category of learning can be classified into two sections: distinguishing between two unseen groups and differentiating between multiple unseen groups. The first section includes algorithms like Siamese Networks , and TriThe second category of learning is prior knowledge of learning, in which models use prior knowledge to necessitate the creation of a generalized model. This category of learning can be classified into three sections: techniques used for hyper parameter tuning, learning update rules and sequence methods using the entire dataset with a test example to estimate the test label's value. The first section is used to build the model with optimal learning parameters and tune the hyper parameters, which include algorithms like Model-Agnostic Meta-Learning (MAML) , FOMAML The third category of learning is prior knowledge of data, in which the model utilizes the variability of data and its structure to create a variable model from just a few example data. This category includes algorithms that can be split into two sections: creation of general model for families of data classes and synthesis of new examples in the training set. The first section includes Pen-stroke models  and NeurAttempts have been made with meta-learning to classify objects and attributes such as Zero-Shot Learning and Generalized Zero-Shot Learning used to classify Caltech-UCSD-Birds (CUB) , Oxford In this work, we shed light on an unattended area, which is the applicability of few-shot learning to image scene classification. Two models were derived from MobileNetV2 for image scene classification. In order to assess their performance, we discovered the shortage of mini-scene datasets that suites few-shot learning. We faced this challenge by proposing two mini-image scene datasets. Different architectures were explored to study the behavior of the scenes mini datasets.The Minisun dataset contains 100 classes randomly chosen from Sun397 with 100 images of size 84 × 84 pixels per class. It is split into 64 base classes, 16 validation classes, and 20 novel classes as shown in The MiniPlaces dataset contains 100 classes randomly chosen from Places with 600 images of size 84 × 84 pixels per class. It is split into 64 base classes, 16 validation classes and 20 novel classes as shown in While FSL requires less data for the training process, researchers commonly use small models with an optimal number of parameters to train and compile the data such as Conv4, Conv6, Conv8, and ResNet-12 . We explIn order to select the best algorithm for parameters generalization, Reptile algorithm was used. It is built by OpenAI—a non-profit artificial intelligence research company—to perform model-agnostic meta-learning . This alThe MobileNetV2 model was selected for possessing the fewest model parameters among the Keras models . FurtherSecond, MobileConv, which is an optimization of layers’ selection from MobileNet and MobileBlock1 by adding conventional layer before the second batch normalization layer and replace the LeakyRelu with Relu from MobileBlock1. The hyperparameters are defined and the models are described in To contribute to the advance of SR research, our work is made available at:https://doi.org/10.6084/m9.figshare.14774718.v1MiniSun dataset: https://doi.org/10.6084/m9.figshare.14774754.v1MiniPlaces dataset: https://github.com/MohmedSoudy/Insights-into-few-shot-learning-for-scene-classification.Data-preprocessing and models: Comprehensive experiments were conducted to assess the accuracy of the proposed models. To investigate the behavior of the datasets with large models, MobileNetv2 is selected as it has the fewest model parameters and depth among the Keras models. Results in Therefore, we redirected the training data into smaller models using different architectures; Conv4, Conv6, Conv8, and ResNet-12. To increase the level of complexity, we added two more layers to Conv6 to get the Conv8 model that achieved 29.32 ± 0.012 for five-shots five-ways and 21.48 ± 0.011 for one-shot five-ways. Results show that the accuracies decreased by 0.139% for five-shots and 0.144% for one-shot compared to Conv6. ResNet-12 is used to confirm the impact of adding more layers and increasing the depth of network, showing an overfitting on five-shots five-ways.These results demonstrate that there is an inverse relation between model depth and accuracy where increasing model depth and layers leads to less accuracy.Contrariwise, the proposed model Mobileblock1 achieved 40.12 ± 0.015 accuracies with five-shots five-ways classification and 30.86 ± 0.013 for one-shot five-ways classification. Furthermore, MobileConv achieved 47.5 ± 0.0158 for five-shots five-ways classification and 30.72 ± 0.013 for one-shot five-ways classification. Notably, the proposed models show the best results with this dataset, achieving accuracies of 47.5 ± 0.0158 and 30.86 ± 0.013 for five-shots five-ways and one-shot five-ways respectively. The proposed models outperform Mobileblock1 by 0.183 % and Conv4 by 0.21% for five-shot five-ways classification.For more clarification and comprehensiveness, we tested the aforementioned models on another dataset; MiniPlaces. We did not use MobileNetV2 as it overfits with MiniSun. The Conv4 model showed accuracies of 27.9 ± 0.014, 29.62 ± 0.013 for five-shots five-ways and one-shot five-ways respectively, while Conv6 achieved accuracies of 19.84 ± 0.007 for five-shots five-ways and 21.42 ± 0.009 for one-shot five-ways, Conv8 achieved accuracies of 25.2 ± 0.011 for five-shots five-ways and 21.14± 0.004 for one-shot five-ways. Unlike MininSun, the accuracies didn't follow a specific pattern as the accuracies decreased with one-shot five-ways but slightly decreases with five-shots five-ways. For more clarification we evaluated the models on ten-shots to show the behavior of our models with the increase of train samples and our models over performed the benchmark models. The proposed model MobileConv achieved the best accuracies of five-shots five-ways with 34.64 ± 0.014 and 26.36 ± 0.013 for one-shot five-ways classification. Results for the proposed model MobileConv strongly correlates for both datasets. Results are annotated in Research in few-shot learning is mainly focused on object detection and classification. In this paper, we explored the usage of few-shot learning in the area of scene classification by implementing two models to classify scenes in images. Those models are evaluated using two mini data sets for validating their performance. Compared to existing models, the proposed models showed significant improvements by achieving accuracies 47.5 ± 0.0158 for five-shots and 30.86 ± 0.013 for one-shot learning on the MiniSun dataset while achieving accuracies of 34.64 ± 0.014, and 26.36 ± 0.013 on the MiniPlaces dataset for five-shots and one-shot respectively. We aim to provide a benchmark and platform for scene classification as a web service to facilitate user-model interaction and help researchers build their models and test them using few clicks.10.7717/peerj-cs.666/supp-1Supplemental Information 1Click here for additional data file.


Internet is a necessary tool of everyday routine, however, concerns about the development of abnormal behaviours in relation to its use by adolescents are constantly growing. The need of brief screening tools for problematic internet use in teenagers in Greece is imperative. The purpose of this study was to validate the 6-item short form of the Problematic Internet Use Questionnaire (PIUQ-SF-6) in a Greek sample of adolescents. The sample consisted of 200 adolescents  who completed the study’s questionnaires i.e., a demographic questionnaire, the PIUQ-SF-6, the Young’s Diagnostic Questionnaire (YDQ) and the Adolescent Computer Addiction Test (ACAT). The internal consistency of the Greek version of PIUQ-SF-6 was sufficient and acceptable . Confirmatory Factor Analysis was significant, and goodness-of-fit was adequate. For establishing convergent validity, Pearson’s and Spearman’s correlation coefficients were calculated between the PIUQ-SF-6 and ACAT scales and Receiver Operating Characteristic (ROC) analysis (between PIUQ-SF-6 and YDQ) indicated an excellent accuracy. The Greek version of the PIUQ-SF-6 demonstrated satisfactory psychometric properties  and is recommended as a reliable screening tool for problematic internet use in Greek adolescents. Over the past decade the use of the internet has increased significantly and has become an integral part of our lives. Internet overuse is a global problem causing growing concern, which has led the scientific community to introduce terms such as “Problematic Internet Use” (PIU) or “Internet Addiction Disorder” (IAD). The term of IAD was first used by the New York based psychiatrist Ivan Goldberg . The clinical psychologist Kimberly et al. found that internet addiction covers a number of behaviours and impulse control problems and can be classified into five different types: (i) Cyber sexual addiction - compulsive use of adult websites for cybersex and cyber porn, (ii) Cyber-relationship addiction - over involvement in online relationships, (iii) Net compulsions - obsessive online gambling, shopping, or online trading, (iv) Information overload -compulsive web surfing or database search, (v) Computer addiction - obsessive computer game playing  in a Greek sample of adolescents.The study was conducted from July to September 2020. The sample was recruited from three Greek cities; Athens , Nafplio  and Nemea . The sample was set to be able to conduct factor analysis. According to the literature, the study sample should either follow the rule of 100, , or fiveParticipants were asked to declare few demographic characteristics such as sex, age, school class, parental educational level, and parental job status. Adolescents’ habits related to internet use were assessed with four questions about i) ownership or not of a personal computer (PC) they used, ii) hours spent online, iii) main reason of internet use, and iv) social media used. Adolescents were also asked to complete three questionnaires, the Problematic Internet Use Questionnaire-short form (PIUQ-SF-6), the Young’s Diagnostic Questionnaire (YDQ) and the Adolescent Computer Addiction Test (ACAT).i.e., adults and adolescents) . The test–retest correlation of the original PIUQ was 0.90 , all having reliable factor structures, and proven validity across both online and written data collection methods in samples of different age groups (escents) . Years lescents) . For theAfter obtaining permission from the developer to validate the PIUQ-SF-6 in a Greek population sample, the forward and back translation stage was carried out. Two bilingual translators, native speakers of Greek, translated the initial version of the PIUQ-SF-6 to the Greek language. They worked separately and each translator produced a written record of a translated version. Afterwards, the principal investigator unified the two translations into a single version since there were no significant differences between them. A third bilingual translator, native English speaker was recruited to translate backward the Greek version to the English language, without having ever read the original questionnaire. A three-person expert committee  supervised the translated versions and finalised the Greek version of the PIUQ-SF-6.A pilot test in 15 participants was conducted to identify any confusing questions or meanings as well as suggestions for possible improvement. Moreover, during the pilot test the principal instigator counted the time needed for completion of the questionnaire (approximately 3 minutes). Reliability analysis, and more specifically Cronbach’s alpha, was carried out in this phase to examine the internal consistency of the PIUQ-SF-6 which was found equal to a = 0.72, an acceptable value as it was higher than the threshold of 0.7 .For convergent validity and for Receiver Operating Characteristic (ROC) analysis the Young’s Diagnostic Questionnaire  was used, which is validated in the Greek population , comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and standardized root mean residual (SRMR) were computed to evaluate the model. The model can be considered satisfactory when the CFI and TLI values are higher than or close to 0.95 and still acceptable if the values are above 0.90 , standard deviation (SD) and median (Mdn) described continuous data. The internal consistency was carried out using Cronbach’s alpha; values above 0.7 were considered acceptable . Exploraove 0.90 . An RMSEove 0.90 . To checove 0.90 .Statistical analysis was performed using the SPSS version 25.0  and R Statistics software version 4.0.3 , and theA total of 200 adolescents aged 12–17 years completed the questionnaires. Participants’ demographic and other characteristics are presented in Specifically, 55.0% (n = 110) of the respondents were male, while females constituted 45.0% of the sample (n = 90). Most students were in high school (61.6%) while 38.4% were in junior high school. Almost 8 out of 10 participants had parents who were high school graduates or had a University degree. Almost 10 out of 10 fathers were employed, whereas 8 out of 10 mothers were in employment. Nine in 10 students had a personal computer at home (25.9% reported exclusive possession and 75.6% shared use with other family members). Regarding the hours they spent on the internet on a daily basis, 17.2% of the participants spent 1–2 hours, 37.9% 2–3 hours, 34.8% 3–4 hours, 8.6% more than 4 hours and the rest 1.5% did not own a computer and did not use internet at all. The main reasons for using internet were to chat with friends or other people (37.9%) and to play games (33.8%). The most popular social media were Facebook (32.6%), followed by Instagram (25.5%) and Viber/WhatsApp (23.4%).The EFA of PIUQ-SF-6 is presented in Regarding the internal consistency of the Greek version of the PIUQ-SF-6, Cronbach’s alpha coefficient was a = 0.80 for the total scale .2 = 358.45, p < 0.001). The goodness-of-fit summary was adequate; TLI = 0.88; CFI = 0.95; SRMR = 0.04. The only index that was not good enough is RMSEA which was 0.12, slightly higher than 0.10.The CFA of the PIUQ-SF-6 is presented in To prove convergent validity, Pearson’s and Spearman’s correlation coefficients between the PIUQ-SF-6 and ACAT scales were calculated .Strongest correlations were found between Control Disorder and Extreme Use , Neglect and Extreme Use , Obsession and Computer Addiction .To measure convergent validity, ROC analysis between PIUQ-SF-6 and YDQ was carried out . For theInternet is a means of communication, entertainment and education that is constantly evolving, exerting a significant influence on the shaping of modern reality. While internet is a necessary tool of everyday routine, the concern for development of pathological behaviours in relation to its use is constantly growing. The negative effects on young people’s physical health, due to the extensive screen time and sedentary lifestyle, internet addiction, excessive involvement with online games and social networking sites are of particular concern to the scientific community. In Greece, digital literacy has not come through a coordinated learning process but through commercial engagement, resulting in high levels of internet addiction , neglect , and control disorder .The PIUQ-SF-6 is a comprehensive, brief tool that assesses three key factors in PIU; obsession  .To conclude, the Greek version of the PIUQ-SF-6 demonstrated satisfactory psychometric properties  and is recommended as a reliable screening tool for PIU in Greek adolescents. Future large surveys are needed to provide more in-depth knowledge about PIU in children and adolescents in Greece.